The World Wide Web (WWW) is comprised of an expansive network of interconnected computers upon which businesses, governments, groups, and individuals throughout the world maintain inter-linked computer files known as web pages. Shoppers navigate these pages by means of computer software programs commonly known as Internet browsers. Due to the vast number of WWW sites, many web pages have a redundancy of information or share a strong likeness in either function or title. The vastness of the unstructured WWW causes shoppers to rely primarily on Internet search engines to retrieve information or to locate businesses. These search engines use various means to determine the relevance of a shopper-defined search to the information retrieved.
The authors of web pages provide information known as metadata within the body of the hypertext markup language (HTML) document that defines the web pages. A computer software product known as a web crawler systematically accesses web pages by sequentially following hypertext links from page to page. The crawler indexes the pages for use by the search engines from information about a web page as provided by its address or Universal Resource Locator (URL), metadata, and other criteria found within the page. The crawler is run periodically to update previously stored data and to append information about newly created web pages. The information compiled by the crawler is stored in a metadata repository or database. The search engines search this repository to identify matches for the shopper-defined search rather than attempt to find matches in real time.
A typical search engine has an interface with a search window where the shopper enters an alphanumeric search expression or keywords. The search engine sifts through available web sites for the shopper's search terms, and returns the search of results in the form of HTML pages. Each search result includes a list of individual entries that have been identified by the search engine as satisfying the shopper's search expression. Each entry or “hit” may include a hyperlink that points to a Uniform Resource Locator (URL) location or web page.
Electronic shopping (or e-shopping) has been gaining popularity as the popularity of the World Wide Web increases. E-shopping continues to evolve from a means of providing an easy way of accessing (and publishing) information on the Internet to a virtual marketplace where almost every merchandise can be traded, as it is in the physical world. As more retail businesses market their merchandise over the WWW, it will become more important for a business to distinguish itself from the competition. One of the significant deficiencies of online retail stores is the amount of shopping advice they can offer. Typically, the shopper does not have access to a sales clerk to accompany him or her in finding the items of choice, or related and matching items.
For example, if the shopper is browsing in a regular real-world clothing store he or she can ask a sales clerk for assistance in finding items. The sales clerk can make recommendations of items that may match or enhance the chosen items. This type of advice is often missing in online shopping stores. Certain online stores try to compensate for this deficiency by offering online chat rooms as an additional service. However, online chat rooms require staffing thus added operational expense. There is therefore a need to automate the online service advice.
In addition, merchants may wish to perform “cross-selling” of goods and services, that is selling related and associated items in addition to the actual sale. In real-world shopping stores sales clerks are able to assist the shoppers by providing useful advice, which might result in additional sales. For instance, a sales clerk may recommend a shirt, and a tie, which match the selected trouser. Rather than selling only the trouser, the sales clerk will sell related additional items and increase the merchant's sales. As pointed out earlier, an Internet-based shopping site does not typically have the possibility of enhancing sales by utilizing cross-selling.
The following are exemplary attempts to provide personalized services in the field of the present invention. For a more detailed description of the services, reference is made to the corresponding web sites.
Broadvision provides solutions in the area of personalization, marketing and promotional tools for web sites. This company's web site enables companies to cross-sell items, that is, selling similar or related versions, or up-sell items, that is, newer versions, to shoppers based on previous purchases in their shopping basket, and communities of which they are members. The main focus and emphasis of Broadvision is an end-to-end application for rapid deployment and dynamic personalization of high transaction volume retail e-commerce sites. However, Broadvision does not use a rule-based approach to automatically generate linkage between different articles.
Dynamo Personalization Server is a rule-driven personalization platform based on the Dynamo Application Server. Dynamo Personalization Server allows targeting specific content to a particular user or group of users based on business rules created by business managers. It combines explicit user data from existing marketing databases with implicit information gathered on user behavior, and other related sources of information. According to the specifications of this product, it does not allow cross-selling based on rules regarding the items and does not offer enabling technology to enhance a database system to provide retail item associations.
The Rules-Based Merchandising engine of the Annuncio Bright product allows marketers to create a new program (for sales, marketing) based on their expertise. According to the product data sheet, the merchandising engine offers the following services: It enables marketers to apply their merchandising expertise to create successful programs. It further features a guided rules builder and supports many criteria, such as shopper profile, product, catalog, services, content. It also encourages mixing of criteria to create dynamic offers.
Though the merchandising engine of the Annuncio Bright product handles the creation of the rules, the result of the rule creation is not an association of items but a set of rules that define how items are related. Furthermore, this product provides and handles rules on the level of items and item categories, but not item attributes, with the items being still associated manually. It would be desirable to have a system and method that apply such a ruleset to an existing database.
The Blaze Advisor product focuses on the creation of “business rules” that are the basis of an application. It allows the creation of rules down to the level of item attributes. As with the rules-based merchandising engine of the Annuncio Bright product, which was discussed earlier, the result of the Blaze Advisor rule creation does not automatically enhance a database. Furthermore, since the rules created by the Blaze Advisor product are used during runtime, they are not independent of the underlying database system. As used herein, “runtime”, means the rules are applied while the program is executed. The alternative would be to precompute the result of applying the ruleset and then access its results during runtime. There is therefore still a need for a method that performs a pre-computation to write association information into a database such that the related items can be easily found and accessed during runtime.